In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

To test the difficulty of our dataset, we checked the majority class label and the accuracies of five state-of-the-art NLI models adopting different approaches: BiMPM (Bilateral Multi-Perspective Matching Model; BIBREF31 , BIBREF31 ), ESIM (Enhanced Sequential Inference Model; BIBREF32 , BIBREF32 ), Decomposable Attention Model BIBREF33 , KIM (Knowledge-based Inference Model; BIBREF34 , BIBREF34 ), and BERT (Bidirectional Encoder Representations from Transformers model; BIBREF35 , BIBREF35 ). Regarding BERT, we checked the performance of a model pretrained on Wikipedia and BookCorpus for language modeling and trained with SNLI and MultiNLI. 
 Question: What NLI models do they analyze?
BiMPM ESIM Decomposable Attention Model KIM BERT

The grammar induction method works on the premise of curriculum learning BIBREF7 , where the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns. A context-free grammar (CFG) is induced from the text, which is represented by several layers of semantic annotations. 
 Question: How did they induce the CFG?
the parser first learns to parse simple sentences, then proceeds to learn more complex ones. The induction method is iterative, semi-automatic and based on frequent patterns

For model distillation BIBREF6 , we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0

where INLINEFORM0 is the cross-entropy function, INLINEFORM1 is the softmax function, INLINEFORM2 is the BERT model's logit of the current wordpiece, INLINEFORM3 is the small BERT model's logits and INLINEFORM4 is a temperature hyperparameter, explained in Section SECREF11 .

To train the distilled multilingual model mMiniBERT, we first use the distillation loss above to train the student from scratch using the teacher's logits on unlabeled data. Afterwards, we finetune the student model on the labeled data the teacher is trained on. 
 Question: How do they compress the model?

we extract sentences from Wikipedia in languages for which public multilingual is pretrained. For each sentence, we use the open-source BERT wordpiece tokenizer BIBREF4 , BIBREF1 and compute cross-entropy loss for each wordpiece: INLINEFORM0

